Podcasts about Product management

The best piece of technology is often the one you don't even notice. When it works, everything runs smoothly and the tech itself vanishes – but how we achieve this is changing dramatically with the power of AI.This week on the show, we're following up our ‘Self-Driving Networks 1-0-1' episode with a ‘Self-Driving Networks 1-0-2': what do these networks look like in practice? Sunalini Sankhavaram, VP Product Management, HPE Networking, joins host Michael Bird to discuss:How machine learning works to help networks anticipate demand and respond accordinglyWhy proactive prevention is replacing traditional troubleshootingHow self driving networks can isolate and treat threats before they have a chance to cause damage

几天前，2026 高通骁龙峰会在夏威夷茂宜岛开幕。高通今年整场峰会就围绕一个核心叙事： Agentic AI 的时代，手机正在从「以 App 为中心」转向「以智能体为中心」。围绕 Agentic AI 的方向，高通在芯片上一口气发了两颗的 2nm 的旗舰芯片，设备形态从手机延伸到了 眼镜、耳机、甚至 AI Pin；还和阿里、 Google等软件厂商合作了 AI 笔记本电脑 。你能感觉到，高通不只是在发一颗芯片，而是想把整个智能体时代的基础设施全部握在手里。 除了带给大家峰会现场的最新发布，我们还想跟大家聊聊三个大的话题：手机、眼镜和耳机这些新的 AI 设备给我们带来了什么？我们有了这么多的 AI 入口，手机的地位会不会改变？还有就是当我们拥有了如此之多的 AI 设备以后，谁来把我们的这些记忆统一起来，更高效地服务我们？ 本期人物 王博：甲子光年 首席内容官 Ziad Asghar：高通产品管理高级副总裁（Senior Vice President, Product Management, Qualcomm） 王吉平：IDC 分析师（聚焦终端业务：PC、平板、手机、可穿戴、机器人等） Shawn：Memories.ai 创始人，前 Meta Reality Labs 时间轴 [01:18] Agentic AI 的体验将从意图开始 CEO Cristiano Amon 宣布手机从 APP 中心转向智能体中心 高通的野心不止芯片，从眼镜、耳机到笔记本，要做 AI 时代的基础设施 [08:51] 手机会被取代吗？ 终端入口变多了，但手机仍是承载个人上下文和隐私信息的中枢 手机从贴身工具升级为 hub，边缘侧算力分发给眼镜等配件 [11:48] 消费者会为新的终端买单吗？ 存储超级周期之下，手机出货量跌 16.7%，安卓均价涨 40% 消费者是否愿意付费要看 AI 溢价能否撑起消费者的涨价承受力 [17:39] Remember → Understand → Act：高通画出个人 AI 路线图 终端持续感知和记录，AI 判断哪些重要、哪些是隐私 Memories.ai 给 AI 装上“视觉记忆”，把海量视频高效 index 成可检索的个人记忆 [25:01] 隐私放在哪？端侧和云端的分工 感知数据和个人记忆留在本地，复杂推理和 agent 任务去云端 “它就是我的另一个大脑，存在我信任的设备里” [27:06] 跨设备互操作标准，高通的软件生态野心 高通正在 Snapdragon 设备间推进互操作协议，目标是开放为行业标准，让非高通设备也能接入 39 亿美元买下 Modular，Mojo 和 MAX 帮助模型在不同硬件上统一部署 幕后制作 监制：Yaxian 后期：迪卡 运营：George 设计：饭团 商业合作 声动活泼商业化小队，点击链接直达声动商务会客厅，也可发送邮件至 business@shengfm.cn 联系我们。 加入声动活泼 声动活泼正在招聘全职商务运营经理、早咖啡内容实习生和社群实习生，如果你也对播客行业的内容制作感兴趣，欢迎点击招聘入口 关于声动活泼 「用声音碰撞世界」，声动活泼致力于为人们提供源源不断的思考养料。 我们还有这些播客：声动早咖啡、声东击西、吃喝玩乐了不起、反潮流俱乐部、泡腾 VC、商业WHY酱、跳进兔子洞、不止金钱 欢迎在即刻、微博等社交媒体上与我们互动，搜索 声动活泼 即可找到我们。 期待你给我们写邮件，邮箱地址是：ting@sheng.fm 欢迎扫码添加声小音，在节目之外和我们保持联系。Special Guests: Shawn, Ziad Asghar, 王博, and 王吉平.

Ghazal Badiozamani is the SVP of Product Management at Cengage, one of the world's largest education companies with roots in publishing that go back over 100 years. Before Cengage, she spent eight years at Elsevier helping legacy businesses understand and adopt emerging technology, starting with machine learning before it was mainstream, eventually building a digital team from scratch and launching a digital authoring system from nothing. In this episode of the CPO Rising series, Products That Count Resident Chief Product Officer Renee Niemi sits down with Ghazal to talk about what it takes to lead product inside a company that has existed for over a century, why the squad model may be approaching a fork in the road, and her take on the most pressing question in AI-era product development: what does product even mean now? In this episode, we cover:  (1:11) Ghazal's path from strategy to product at Elsevier, and what prompted the move to Cengage (4:32) What it actually feels like to lead product inside a company that's been around for generations (6:08) Why product problems always end as people problems, and why that's the most interesting part of the job (7:28) How design principles and core values serve as anchors through both digital transformation and AI adoption (12:59) AI's progression: from efficiency tool to fundamental question about what product even is (14:55) Why building for nonlinear human thinking is the new product challenge (16:58) Team structure today and two very different futures for the squad model (19:46) Why removing design from the triad is a mistake, and what each personality type actually contributes (23:23) Development speed: from 3 to 5 months down to 2, and why customer absorption is now the real constraint (27:14) The CPO alpha effect: the two capabilities that are almost never found in the same person (30:45) The funnel-based product analytics dashboard Ghazal checks almost every day Blog and detailed workflow walkthroughs from this episode: https://productsthatcount.com/the-question-ai-still-cant-answer-about-product-management-cengage-product-svp/

After a full series exploring what product managers should retain, and rethink, in the age of AI, Matt and Moshe sit down for an honest wrap-up. What did we learn, what changed our minds, and where do humans still matter most?In this special episode, we look back at the conversations on human judgment, empathy, vision, strategy, and stakeholder alignment, then connect them to our own day-to-day experiments with AI. Moshe shares what he learned while building and publishing his new mobile app, Mental Health Wallet, using tools like Kiro. Matt reflects on building an agentic orchestration product, and where AI has genuinely helped sharpen strategy and execution.The big takeaway?AI can dramatically accelerate product work, but speed, output, and confidence are not the same as understanding. The PM role is not disappearing. It is shifting toward stronger context-setting, better judgment, deeper customer understanding, and more intentional collaboration.Join Matt and Moshe as they reflect on:Rebecca Douglas's conversation on human judgment and empathy, and why product managers still need to interpret ambiguity, understand users as people, validate AI outputs, and decide what not to buildJohn Fontenot's perspective on vision and strategy, including why AI can be a powerful thinking partner but cannot fully own long-term direction, strategic tradeoffs, market context, or business accountabilityJoy Adamson's insights on stakeholder alignment, and why trust, relationship-building, incentives, organizational dynamics, negotiation, and emotional intelligence remain deeply human workHow AI can support product work through research synthesis, organization, ideation, prototyping, requirements, competitive exploration, and strategic feedbackWhy AI-generated advice can sometimes feel like generic validation, and how better context and sharper prompts lead to more useful strategic feedbackWhy market context, competitive insight, and business goals still need to come from humansThe changing balance between AI and human involvement across different product activities:Human judgment and empathy remain deeply human-ledVision and strategy can be increasingly AI-assisted, but remain dependent on human contextStakeholder alignment is heavily human, because trust, incentives, relationships, and politics cannot be fully automatedWhy user interviews and real customer conversations remain more valuable than AI-generated “users” or synthetic feedbackHow the importance of human involvement may shift by company stage, team size, and the number of stakeholders involvedThe potential future of product work, including AI agents, agent marketplaces, new workflows, and changing expectations for PMsAnd much more!You can connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcast Matt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovsky Note: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

"I think one of my biggest problems is I finish every race and think, 'I think I can go faster!'" If you follow the sport, you probably recognize Alana Levy. The New York City-based runner and member of Brooklyn Track Club is seemingly everywhere these days. From local staple races like the New York Mini 10K and the Fifth Avenue Mile (where Alana just won the open division in a personal best 4:38) to major marathons, like the 2025 Chicago Marathon where Alana earned her Olympic Trials Qualifier, running 2:36:53, to this year's Boston Marathon, where she improved her personal best by more than two minutes, racing in the pro field and finishing in 2:34:50. She's logging 100-mile weeks, winning races, and qualifying for the Olympic Trials Marathon — while working full-time as the director of product management at Pearl Health. In this conversation, Alana talks about her relationship with running and how it has evolved over the years, and about what it's like being a part of the ever-evolving New York City running community.  FOLLOW ALANA @yo_lans SPONSOR:  New Balance. Click here to get your hands on the just-released SC Rebel. You're going to love it! Lagoon. My favorite pillows and pillowcases! Click here to get the limited edition Ali on the Run x Lagoon Cool Flex Performance Pillowcase (!!!), and use code ALI at checkout for 15% off. IN THIS EPISODE: What's making Alana happy today, and what she's into right now (3:20) All about Alana's job as Director of Product Management at Pearl Health (14:55) What Alana's relationship with running is like right now (16:55) Alana reflects on "Young Lans," her childhood in Chappaqua, NY, and her experience running in college (18:40) On graduating from college and trying to figure out what to do next (29:00) How Alana has found her place in the New York City running community (35:30) How Alana became a 2:34 marathoner (41:25) Alana's OTQ journey (44:50) What it was like racing the 2026 Boston Marathon in the pro field (56:40) What Alana's relationship with running looks like right now (1:04:20) Follow Ali: Instagram @aliontherun1 Subscribe to the newsletter Join the Facebook group Support on Patreon SUPPORT the Ali on the Run Show! If you're enjoying the show, please subscribe and leave a rating and review on Apple Podcasts. Spread the run love. And if you liked this episode, share it with your friends!

Every product team is shipping AI. But the dirty secret is that, for many companies, very few users are adopting. The feature launches, the people who try it love it, and adoption stalls anyway. Our guest today thinks that's because teams obsess over building AI and barely think about adoption. Charanya Kannan is VP and GM of Navan Anywhere, which puts Navan's AI travel and expense tools inside Slack, Teams, and Gemini, where people already work, instead of asking them to open another app. She joined Navan when the company was still called TripActions and doing under $50 million in revenue... now it's well on its way to a billion. Her take: if customers have to work harder to use your AI, it isn't actually better. In this episode, Charanya shares: How Navan Anywhere was built distribution-first, bringing booking and expenses into the tools people already use every day Why Navan's margins went up in the AI era, while the rest of SaaS braces for compression And why she believes PMs who mostly manage process and Jira tickets will fade away, while those who can actually drive user benefit and business outcomes will matter more than ever Links LinkedIn: https://www.linkedin.com/in/meetcharanya/ Navan: https://navan.com/ Chapters 00:00 Introduction 04:17 Why AI features don't always get adopted 06:30 Building Navan Anywhere distribution-first 10:13 When to use AI vs. deterministic systems 12:01 Conversational cognitive load and why good UI still matters 15:57 Evals, distilled models, and the AI reliability pyramid 20:14 Product owners vs. product managers in the AI era 26:10 How Navan is growing margins while SaaS faces AI compression 31:40 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Charanya Kannan.

AI isn't just changing how we use software, it's changing who software is built for.In this episode of Lessons in Product Management, John Fontenot sits down with Kimberly Logan, Head of Product at the Walrus Foundation and former Google leader, to explore the evolution of product leadership and what happens as AI agents increasingly become users of software themselves.Kim shares lessons from her transition from technical program management into product leadership, including the shift from focusing on execution and the “how” to owning the “what” and “why.” They also discuss product-market fit, building better customer feedback loops, prioritizing under resource constraints, and how AI can make strong product managers even more effective.Then the conversation turns toward the emerging infrastructure of an agentic world: persistent AI memory, data ownership, portability between AI systems, verifiability, and the trust problems that arise when agents begin operating across organizational boundaries.What does product management look like when intelligence becomes cheap, but trust becomes increasingly valuable? That's the question at the center of this conversation.Outline:00:00 — Introduction & Kimberly Logan's background 01:29 — From Technical Program Manager to Product Leader 05:15 — Making the transition into Product Management 07:10 — Skills aspiring PMs should develop 09:40 — AI as a force multiplier for Product Managers 10:35 — Is AI a tool or a fundamental technological shift? 12:31 — What happens when AI agents become the users?13:58 — What is Walrus?15:26 — Why your data becomes more valuable in the AI era 17:40 — Portable, programmable & verifiable data18:44 — Solving AI platform risk with portable memory 20:47 — Verifiable AI memory21:50 — Where AI memory is being used today24:44 — Finding real Product-Market Fit 26:18 — How do you prioritize with limited resources? 28:32 — Ruthless prioritization & product trade-offs 30:16 — Where AI goes next 31:45 — The cross-company trust problem for AI agents

Diese Episode wird unterstützt von Localo, der Local SEO Plattform: https://localo.com/de/--------------------------Link zum besprochenen Artikel: https://searchengineland.com/geo-pillars-488782---------------------Ist GEO nur SEO mit neuem Namen? Olaf Kopp (Aufgesang) hat in seinem Search-Engine-Land-Artikel ein Modell mit drei Säulen für Generative Engine Optimization vorgestellt: LLM Readability, Brand Context und Agentic Commerce. Mit Björn Darko spricht er darüber, welche Säule klar SEO ist und wo SEO kaum noch Einfluss hat. Und er erklärt, warum GEO am Ende vor allem eine Frage der Organisation ist.Es geht um Chunking und Passage Relevance, um eine Studie zum Zusammenhang zwischen Readability und Rankings, um Brand-Context-Audits und Prompt-Tracking und um MCP, UCP und ACP als Infrastruktur für KI-Agenten. Außerdem diskutieren die beiden, warum die LinkedIn-Debatte „GEO vs. SEO“ oft am Thema vorbeigeht.TakeawaysLLM Readability ist die Säule, die am klarsten zu SEO gehört: Crawling, Indexierung, Informationsarchitektur und gut strukturierter Content bleiben die Basis. LLM-Bots rendern oft kein JavaScript und arbeiten noch effizienzgetriebener als klassische Crawler.Grounding baut auf Rankings auf: Wer nicht in den Top 10 bis 20 steht, wird kaum zitiert. Auf Top-Positionen entscheiden Passage Relevance, Informationsdichte und Information Gain.Brand Context entsteht vor allem außerhalb der eigenen Website: Reddit, Communities, Listicles und Third-Party-Mentions. Das liegt in PR, Kommunikation und Brand und nur begrenzt bei SEO.Brand wird messbar: Mit Prompt-Tracking und Brand-Context-Audits lassen sich Positionierung, Attribute und Lücken konkret sichtbar machen.Agentic Commerce braucht API- und MCP-First-Denken: Produktkontext, Feeds, Agent Experience (AX) sowie Hürden wie Login-Walls und Checkouts. Das sind Aufgaben für Product Management und Engineering.Das eigentliche Problem sind organisatorische Silos. Digital Authority Management soll SEO, Content, PR und Brand zusammenbringen.Kapitel00:00 Intro: Olaf Kopp und die 3 GEO-Säulen00:55 Sponsor02:13 Ist GEO eine neue Disziplin?02:57 Retrieval: Crawling, Indexierung und Document vs. Passage Relevance03:50 Wie LLM-Bots arbeiten: Effizienz, kein Rendering, Vektordatenbanken05:26 Brand Context, Reputation und Topical Authority05:45 GEO als Kanal: LLMs als neue Interfaces07:24 Säule 1: LLM Readability08:11 Studie: Readability-Faktoren und ihre Korrelation mit Rankings09:56 Passage-based Indexing, Featured Snippets und Chunking11:23 SISTRIX Newsflash14:11 Content als größter Hebel: Answer Structure und Information Gain16:37 Säule 2: Brand Context17:30 Citations, Mentions und Prompt-Tracking19:53 Empfohlen werden: Listicles und Reputation20:31 Warum SEO beim Brand Context wenig in der Hand hat22:10 Aus der Praxis: Brand-Context-Audits24:25 Silos, Ego und Digital Authority Management27:48 „It's just SEO“? Warum SEO allein nicht reicht29:32 Säule 3: Agentic Commerce31:20 Agentic Readiness: Product Context, AX, MCP, UCP und ACP33:59 Produktdaten, Feeds und Engineering-Hürden37:22 Die GEO-vs.-SEO-Debatte40:43 Name, Mindset und probabilistische Systeme43:11 Bots unterscheiden: Training vs. User-triggered43:33 Olafs Weg: vom Performance Marketing zur Brand44:25 Outro

Toast was built long before generative AI existed. So how do you weave AI agents into a platform that thousands of restaurants already run their business on, without breaking the thing that works? In this episode of The Product Podcast, Carlos (CEO at Product School) sits down with Maggie Crowley, VP of Product at Toast, to unpack what it actually takes to build AI into mature, pre-AI SaaS.Maggie is a former Olympic speed skater (fifth at the 2006 Winter Olympics) turned product leader, with an MBA from Harvard and product roles across Drift, TripAdvisor, and Charlie Health before Toast. She was also a Product School contributor years ago, back in her Drift days, which is what makes this a fitting return. At Toast she leads product on Toast IQ, the company's AI layer for restaurant and retail operators. In this conversation, she and Carlos get into the genuinely hard parts: why the quality bar for building on an established platform is punishingly high, how her team actually shipped their first assistant (sitting in WhatsApp groups with real customers and turning it on for a few of them), and why you have to physically bring the AI to the user instead of assuming they'll discover it.Maggie is refreshingly blunt that restaurant operators have zero tolerance for "AI for AI's sake." They don't care whether it's Anthropic, OpenAI, or an open-weight model under the hood; they only care whether you helped them run their business. She explains how the tight P&L of a restaurant forces real discipline about what's worth charging for, how Toast IQ Grow (an agent that drives demand and fills seats) became a breakthrough use case by slashing the cost of marketing work, and why she still manages a frontline team directly to stay close to how AI is changing the way products get built. The episode closes with a live demo of Toast IQ.What you'll learn:- How to build AI agents into a platform designed years before AI, without lowering the quality bar- The scrappy build process: WhatsApp groups with customers and turning an assistant on for a few- Why you have to bring the AI to the user, and why AI onboarding is an unsolved problem- Why context (where the user is, what time, what role) is everything for an AI product- Why restaurant operators have zero tolerance for "AI for AI's sake"- How the tight P&L of a restaurant enforces discipline about what's worth building and charging for- How Toast IQ Grow uses AI to cut the cost of marketing and drive real demand- Why abstracting model choice (Anthropic vs. OpenAI vs. open weights) away from the user matters- Why a product leader should keep a hand in how teams build, and manage close to the work- A live look at Toast IQ answering real operator questions in plain languageConnect with Maggie Crowley:VP of Product, ToastLinkedIn: https://www.linkedin.com/in/maggie-crowley-42a97112/Host: Carlos, CEO at Product SchoolLinkedIn: https://www.linkedin.com/in/villaumbrosia/About Toast: Toast is an all-in-one platform built for restaurants and retail, spanning point of sale, payments, operations, and marketing, now including Toast IQ, its AI layer for operators.About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.Social Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

About a year ago, the Future of Everything team was in New York taping a special episode on the innovation economy in front of a live audience, and today we're re-releasing it. We sat down with computer scientist Fei-Fei Li and economists Susan Athey and Neale Mahoney to dig into how AI is reshaping creativity, jobs, education, and public policy, and where it might take us next. It's a wide-ranging, energetic conversation. Whether you're thinking about how AI might reshape your own work, or you're just curious where some of the smartest people in tech and economics think this is all headed, this one's a great listen.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profiles: Fei-Fei Li | Neale Mahoney | Susan AtheyConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces this re-release of a live episode featuring Fei-Fei Li, Susan Athey, and Neale Mahoney on the future of the innovation economy. (00:00:45) Defining the Innovation EconomyHow new ideas, technologies, and commercialization are reshaping economic growth.(00:02:13) AI as a General-Purpose TechnologyWhat past technologies like electricity and personal computing can teach us about AI adoption. (00:03:23) The Bottlenecks to AdoptionWhy digitization, software costs, training, and organizational change can slow the spread of new technology.(00:06:05) AI and the Labor MarketWhy uncertainty about which jobs AI will disrupt makes social safety nets especially important.(00:08:04) Augmenting Human WorkWhy Fei-Fei Li sees AI as a tool for enhancing human capabilities rather than simply replacing jobs. (00:11:17) Shaping Human-Centered InnovationHow innovation can be designed to complement human skills, creativity, and purpose.(00:12:21) Universities and AI InnovationHow universities can help develop human-centered technologies and lower barriers to adoption.(00:13:45) Government and the AI TransitionHow public investment and policy could help workers adapt while expanding services like healthcare and childcare. (00:15:50) Rethinking EducationWhy AI may force a fundamental rethink of what and how students learn.(00:17:18) Jobs, Adaptation, and Safety NetsWhat history suggests about how economies adjust to technological disruption—and when policy intervention matters. (00:18:33) AI Regulation and InnovationHow policymakers might balance technological progress with appropriate guardrails. (00:21:57) Competition and Market PowerWhy competition, open models, and the cost of AI access matter for innovation and economic opportunity. (00:24:57) Economic OptimismWhy confidence in the American economy has declined and what might help restore it. (00:26:40) Future In a MinuteRapid-fire Q&A: hope for the future, education, robotics, AI accessibility, and the skills the panelists would learn next.(00:30:24) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

“The networks may change dramatically behind the scenes, but calls still need to connect reliably.” In this Technology Reseller News podcast, Connie Hartman, Director of Product Management at TNS, discusses the ongoing transition from legacy TDM and PSTN infrastructure to all-IP communications—and the routing, interconnection and reliability challenges that come with it. TNS is a global communications technology company supporting wireless, wireline and VoIP operators across call routing, interconnection, numbering intelligence, fraud prevention and call authentication. The company analyzes roughly 1.9 billion call events every day. Hartman says the industry cannot simply switch off the legacy network. During the transition, TDM and IP environments will continue to coexist, requiring carriers to understand where calls should go and how they should be routed across different interconnection environments. That makes the evolution of the LERG Routing Guide particularly important. The industry has traditionally relied on the LERG as an authoritative source of routing information. TNS is now evolving that framework so carriers can publish and access IP routing information while continuing to support legacy routing during the migration. “The goal is to give carriers a practical migration path rather than forcing the industry to rebuild everything from scratch,” Hartman says. The FCC is also working with the industry on the move toward an all-IP future. Hartman says the transition must address much more than retiring old switches. Network resilience, public safety, accessibility, numbering, robocall mitigation and operational governance all need to continue functioning throughout the transition. TNS is supporting that migration with managed routing, voice peering, call authentication, fraud mitigation, STIR/SHAKEN, E911, N11 and 988 capabilities. Hartman expects IP routing and interconnection to remain major industry and regulatory topics through 2027 and beyond, particularly for smaller and regional carriers that need a transition model that works without requiring a disruptive rip-and-replace approach. Ultimately, she says, success will mean making the transition largely invisible to the customer. Visit TNSI.com to learn more.  

Guillaume, VP Content Marteting chez Shine I Cegid, nous explique ce qu'il attend des équipes PMM pour réussir la collaboration et les process mis en place.Dans l'épisode complet découvrez :Au programme :

In this episode, Christina Berg, RN, MHA, Sr. Director, Product Management, Imprivata, discusses how stronger patient identity can improve safety, protect against fraud, reduce duplicate records and denied claims, and create more efficient healthcare experiences. This episode is sponsored by Imprivata.

Jared Roberts is the AVP of Underwriting Operations at Risk Point with 20 years of hands-on experience spanning commercial & personal lines pricing, underwriting, and claims. A lifelong learner, he has earned the CPCU, API, AIC, and CASA designations and shares his expertise as a consulting author for IRMI. Today, Jared joins Chris Hampshire on In The Know for a look at Jared's career path, his thoughtful approach to changing roles, and his involvement with the Emerging Leaders Committee of the CPCU Society.   Key Takeaways ● Jared's insurance career journey started with rental cars. ● Major appealing features of the insurance sector. ● Claims as an ideal training ground. ● Advancing from analyst to product manager. ● Questions to ask when considering a career move. ● The value of earning designations. ● The impact of technology on the operations sector, personal lines, and commercial lines. ● Jared's involvement with the CPCU Society. ● Advice to anyone who is interested in joining a committee. ● Why insurance? Jared has an exciting answer. ● A five-year look to the future of the industry. ● Jared's advice to his early-career self.   In the Know podcast theme music written and performed by James Jones, CPCU, and Kole Shuda of the band If-Then.   To learn more about the CPCU Society, its membership, and educational offerings, tools, and programs, please visit CPCUSociety.org.   Follow the CPCU Society on social media: X (Twitter): @CPCUSociety Facebook: @CPCUSociety LinkedIn: @The Institutes CPCU Society Instagram: @the_cpcu_society   Quotes ● "As soon as I started in claims, I could see that I was doing good for people." ● "Insurance doesn't only provide a societal good; it provides jobs for people." ● "A lot of people in my career have taken a chance on me, and I have the moral responsibility to pass that on through the CPCU Society." ● "I wish I had gotten involved with CPCU designations a lot earlier than I did."  

Your work's forced ranking bell curve wasn't discovered in experiments with people. It was installed via fiat. ...and forced ranking pays people to hide bad news.Product Manager Brian and Enterprise Business Coach Om tear apart the forced ranking system GE made famous, Microsoft ran for a decade, and that Enron perfected. By the end you'll know the four jobs forced ranking secretly does, why none of them even need a quota, and what to ask before your next "calibration" meeting.Listen or Watch to learn:• How forced ranking started as a patch for lazy raters• How Microsoft's lost decade and Enron's fraud both trace back to stack ranking's perverse incentives• Why quotas punish the people for systemic issues• How quotas plus bias create disparate impact and legal exposure• The four jobs forced ranking is doing that keep it aroundThis podcast is for product managers, team members, and managers who suspect the forced ranking quotas may not just be fraudulent, but also actively harmful as they "manufacture" low performers.#ForcedRanking #PerformanceManagement #AgileJack Welch, GE, Enron, Microsoft, W. Edwards Deming, Vanity Fair, Adobe, Bond 2025, Scullen et al. 2005, arXiv:2512.06583LINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

In this episode, Christina Berg, RN, MHA, Sr. Director, Product Management, Imprivata, discusses how stronger patient identity can improve safety, protect against fraud, reduce duplicate records and denied claims, and create more efficient healthcare experiences. This episode is sponsored by Imprivata.

In this episode of the Pure Report, we welcome back Sr. Director of Product Management, Cody Hosterman for a deep dive into the latest cloud innovations. Cody recaps his recent travels to VMware Explore and customer roadshows in Singapore, where discussions centered around AI infrastructure, modern virtualization, and data sovereignty. Our initial conversation sets up a detailed look at how organizations are evolving their hybrid cloud strategies and managing data across diverse environments. Our main focus is the expansion of Everpure Cloud Azure Native to support Azure virtual machines. Cody explains how this managed service delivers enterprise data storage directly inside the Azure portal through native Azure Resource Manager APIs. By extending beyond Azure VMware Solution to native Azure workloads, customers gain access to thin provisioning, deduplication, and compression. These features significantly reduce cloud storage costs and eliminate the need to over provision raw block storage. Beyond storage efficiency, our discussion covers practical integration details and deployment advantages. Cody highlights how the Azure extension simplifies multi-pathing, while full availability in the Azure Marketplace allows purchases to count toward existing cloud spending commitments. Our chat also explores how teams can leverage Azure Red Hat OpenShift and KubeVirt for modern application delivery, proving that intelligent data management is key to unlocking agility and long term value in public cloud environments. To learn more, visit: https://techcommunity.microsoft.com/blog/partnernews/introducing-everpure-cloud-azure-native-for-azure-vms/4539461 Check out the Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Coming Up and Intro 01:23 Travels with Cody 05:49 Update on Azure Momentum 08:50 Data and Cloud Cost Optimization 12:45 Everpure Cloud Azure Native 15:52 Azure VMs 17:42 Advantages of the Service 22:45 AI and Everpure Cloud 25:55 Extending API Services in Azure 29:55 Hot Takes

AI agents aren't just answering questions anymore — they're executing workflows, modifying data, and making autonomous decisions across your most critical systems. As AI agents take on more complex tasks operating at scale and encounter unexpected situations, establishing robust governance and recovery mechanisms becomes increasingly important. Most organizations currently lack the systems needed to manage and address these scenarios effectively.In this episode of the AWS re:Think Podcast, hosts Malini Chatterjee and Jay Sampath sit down with Swami Ramany (GVP, Product Management) and Rob Sadowski (VP, Product & Tech Marketing) from Cohesity to explore the emerging discipline of Agent Resilience — the missing recovery layer for agentic AI at scale. Find further details and demo click HERE Guest: Swami Ramany GVP, Product Management, Cohesity and Rob Sadowski VP, Product & Tech Marketing, CohesityHosts: Malini Chatterjee & Jay Sampath

Most of the AI products getting hype right now are built for people like us, the tech industry. But your customers don't care how the AI works — they just want their problem solved in a way that's easy for them. Brian McMullin, SVP and Head of Product at Network Solutions, has spent 15 years building for small businesses at companies including HubSpot, ezCater, Wayfair, and SamCart. In that time, he's yet to meet a business owner who wakes up thinking, "I can't wait to use AI today." For many of them, a prompt box might as well be a terminal window. So his team doesn't ship the box. They ship the finished work artifact, and all the user has to do is claim it. In this episode, Brian shares: Why doing the work for small businesses first, instead of handing them an empty prompt box, is what gets them to try AI His warning about baking inference into the core of your product before token pricing settles — and the two models he's testing to find out what small businesses will actually tolerate And what SamCart taught him about churn: customers left because basic things were broken, not because features were missing. Links LinkedIn: https://www.linkedin.com/in/brianemcmullin/ Network Solutions: https://www.networksolutions.com/ Chapters 00:00 Introduction 00:48 Brian's product journey from developer to SMB product leader 02:42 How small businesses went from distrusting AI to have AI FOMO 06:10 SMBs don't wake up excited to use AI, they want to close the deal 08:42 Stop showing the agent, ship the finished work 11:48 Inference costs break the old SaaS margin math 15:00 Testing free credits and tiered usage with small businesses 16:47 What usage-based pricing does to ARR and predictability 22:28 Churn is a pile of micro-annoyances, not one event 25:06 What SamCart's churn data revealed about broken basics 28:55 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Brian McMullin.

BONUS: How Scrum Masters Can Use AI Without Losing the Human in the Loop AI makes it easier than ever to build software, prototypes, courses, and coaching tools. In this BONUS episode, Mike Lyons and Greg Pfister share what that speed changes for Scrum Masters, why product judgment becomes more important, and how coaches can start using AI without outsourcing the conversations their teams still need. When AI Stops Being a Curiosity and Starts Saving Real Time "It's not that the work is wrong or not needed, it is needed. That's an important step. Retrospectives are critical."   Greg's first practical AI moment came while trying to build an "Ask Mike" capability for self-paced courses. After a frustrating outsourcing attempt, he started using ChatGPT to help him rebuild the tool himself, eventually moving into Cursor, Claude Code, and the Superpowers plugin for Claude Code. Mike's moment was less technical: using AI inside Mural to affinity-map retrospective notes. The lesson for Scrum Masters is not that AI removes the work, but that it can remove enough friction to let facilitators spend more time on judgment, listening, and follow-through. The Bottleneck Moves From Building Fast to Building the Right Thing "The cost to produce prototypes for software engineering is approaching zero."   Mike and Greg argue that AI does not create the "wrong feature" problem, but it makes the problem much easier to multiply. If a prototype can appear before lunch, the old excuses disappear. Teams still need to ask whether the customer problem is real, whether the payoff matters, whether there is proof from users or data, and whether this work deserves priority now. For Scrum Masters and Agile coaches, this is a clear invitation to help Product Owners slow down the decision before accelerating the delivery. Building AskMe With AI as the Engineering Partner "I'm really playing product manager. That's really what I'm doing."   Greg describes AskMe as an AI-enabled coaching tool embedded into training courses. Instead of asking learners to pass obvious multiple-choice quizzes, AskMe asks them to apply what they learned to their own context, then reflects back practical coaching based on the course material, instructor context, and learner profile. In their own product development, Greg uses AI as an engineering partner while Mike keeps asking the product question: should we build it? Their 4P lens is simple: problem, payoff, proof, and priority. The Scrum Master Role Becomes More Important, Not Less "Don't just outsource your brain, your decision making power."   When leaders push teams to "adopt AI," Mike warns Scrum Masters not to let the tool become the decision maker. AI can cluster retrospective notes, summarize long threads, propose learning plans, or help prepare for a hard conversation, but the human still needs to inspect the output and understand the consequences. Greg adds the practical security angle: teams must be careful about what they paste into AI systems, especially personal, customer, or sensitive company information. Start Small: Context, Role Play, and Shared Learning "Context is king when you're talking with your AI."   Greg suggests starting with basic AI training, then practicing with small workflow improvements: prioritizing work, summarizing material, or drafting communication that the Scrum Master then edits. Mike's practical starter experiment is role play: describe a difficult team situation without names, ask the AI to act as the other person, and practice the one-on-one conversation. Vasco adds a simple working habit: keep a running context file with meeting notes, team insights, worries, decisions, and open questions, then use that context when asking AI for help. Resources for Scrum Masters Learning AI "Let AI help you get smart about AI."   Mike recommends the PMI AI in Project Management learning resources and the 37signals Rework podcast for pragmatic thinking about how AI fits into work. Greg recommends learning directly from the AI tool providers, exploring how to configure projects and context, and reading Marty Cagan's Inspired to strengthen the product judgment that becomes more important when teams can build faster. About Mike Lyons and Greg Pfister Mike Lyons and Greg Pfister are the team behind KaiRise, where they've used AI to build new products, including AskMe, an AI coaching tool, and to create their most recent certified Product Management training course end-to-end.   Greg Pfister works with Mike at KaiRise on AI-enabled learning products, including AskMe and their certified Product Management training course.   You can link with Mike Lyons and Greg Pfister on LinkedIn. You can find KaiRise and AskMe at kairise.com.

On this episode, I speak to the returning Rich Mironov, longtime product management consultant, product leadership coach, and founder of Mironov Consulting. Rich has spent decades working with product leaders, particularly in complex B2B organisations, and is the author of The Art of Product Management and Money Stories. We explore what the current wave of AI-enabled software development really means for product management: why faster code does not automatically create more revenue, where product judgement becomes more important rather than less, and why the role of product may shift away from delivery management towards deeper discovery, commercial thinking, and go-to-market work. Episode Highlights Faster software development does not mean faster revenue growth - AI may allow teams to generate code far more quickly, but customer budgets, market size, and demand do not expand at the same rate. More output only creates business value when it translates into adoption, differentiation, and revenue. More products can mean more competition, not more opportunity - If it becomes dramatically easier for everyone to build software, markets may fill with more entrants and more features competing for the same customers. That can increase price pressure and make commercial differentiation harder rather than easier. User attention becomes the scarce resource - Shipping dramatically more features pushes the burden of prioritisation onto customers, who have limited time and little interest in evaluating a constant stream of changes. Product teams still need to decide what matters enough to build, explain, and promote. AI can automate obvious work, but judgement becomes more important as risk increases - Straightforward bugs and low-risk tasks may be good candidates for automation. As the size and impact of a change grows, teams still need to consider customer value, product coherence, economics, and the wider consequences of getting it wrong. Not every customer request should become a feature - Faster implementation makes it tempting to connect feedback directly to delivery, but many requests are contradictory, poorly framed, commercially harmful, or only relevant to a small subset of users. Product management still requires deciding what NOT to build. Today's product can quickly become tomorrow's feature - Lower development barriers make it easier for competitors and platform vendors to absorb standalone capabilities into broader products. Being able to build something is not enough; teams need a defensible reason why it should exist as a product in its own right. Organisational incentives will shape how product managers use AI - If companies reward visible AI usage, coding, prototyping, or token consumption, product managers will naturally move towards those activities. That does not necessarily mean those activities represent the highest-value use of product management time. Product management may become more 'barbell shaped' - As engineering needs less day-to-day coordination, product managers may spend less time in the middle of delivery and more time at the edges: understanding customers, markets, economics, and strategy before development, then supporting positioning, pricing, sales, and adoption afterwards. AI transformation is an organisational problem, not just a tooling problem - Previous transformation waves showed that counting activity or mandating tool adoption does not necessarily improve outcomes. The larger opportunity comes from redesigning how work flows through the organisation and identifying where genuine bottlenecks and leverage points sit. Commercial accountability needs to extend beyond Product and Engineering - If engineering can deliver more quickly, Sales and Marketing also need credible plans for turning that increased capacity into demand and revenue. Product leaders should connect roadmap expansion to explicit assumptions about markets, leads, quotas, adoption, and economic value. Check out the Article mentioned in the interview, "AI Transformations And Agile Transformations Rhyme":  https://age-of-product.com/ai-transformations-agile-transformations-a3-delegation-system/ Connect with Rich LinkedIn: https://www.linkedin.com/in/richmironov/ Website: https://www.mironov.com Money Stories Book: https://www.mironov.com/book/ Previous Episodes with Rich Fighting Fires in B2B Product Management: https://www.oneknightinproduct.com/rich-mironov Product Managers Need to Understand the Language of Money: https://www.oneknightinproduct.com/rich-mironov-money Work with Me Through One Knight Consulting, I help product companies identify growth opportunities and build the capability to pursue them. If you'd like to chat about how I can help you, e-mail me at hello@oneknightconsulting.com or book a free advisory call here: https://okip.link/advice

On prend du recul sur le Product Marketing à travers le regard d'un partenaire clé : le Content Marketing.Cette conversation avec Guillaume, VP Content Marteting chez Shine I Cegid, vous aidera à mieux comprendre les attentes des équipes Marketing, les ingrédients d'une collaboration efficace avec les PMM et l'évolution de la fonction dans un contexte marqué par l'IA et la transformation des organisations.Au programme :

Jonathan Evens is a product lead at Google DeepMind, where he has spent more than a decade applying machine learning and AI across industries — from the smart grid at AutoGrid, to detecting roads and buildings from satellite imagery at Planet, to recommender systems, Google Search's AI Overviews and AI Mode, and now live avatars. He is also an advisor to the Evens Foundation, where he is building a "digital citizenry": a democracy sandbox that uses synthetic citizens to pre-test how the public might react to a policy before it is written.Jonathan returns to The Product Experience, where hosts Lily Smith and Randy Silver pick up the conversation they started at MTPcon London, to dig further into what actually separates an AI product manager from a product manager who simply uses AI tools, why product principles have to come before evaluations, and how synthetic users can help — and mislead — at very different scales of product.We discuss:1. Why "AI product manager" has become a near-meaningless label, and the two distinct roles hiding underneath it: the modelling product manager working on core model capabilities, and the AI feature product manager building AI-powered products2. Why using an LLM as a thinking partner or a coding assistant does not make someone an AI product manager — it makes them a product manager using AI tools, full stop3. How Google Search's North Star metrics have stayed constant even as the proxy metrics beneath them — side-by-side win rates, user ratings, RLHF signals — have had to be rebuilt from scratch4. Why product principles, not evaluations, are the real starting point for any AI feature, and how Google Search resolved the problem of trustworthy sources disagreeing on basic facts5. How Google Search builds trust into its AI Overviews through sourcing, citation placement and UX cues such as highlighting, so users can judge at a glance what to verify6. Where synthetic users genuinely help — cold-start problems, privacy-sensitive research, automated regression testing — and where they fall short7. Building the Evens Foundation's "digital citizenry", and the core technical problem behind it: AI-generated personas that are less diverse and more extreme than real people8. How team size and structure differ between a fully resourced lab like Google DeepMind and a resource-constrained non-profit team, and why Jonathan resists a single answer for the "right" team size9. How the product manager's job is shifting as engineers absorb more of the evaluation work themselves through prompting and iteration10. Jonathan's advice for product managers building AI features, and his case for following the Makers Manifesto Key takeaways"AI product manager" covers two distinct jobs. The modelling product manager defines and measures a model's core capabilities — factuality, reasoning, long context — and that role is concentrated almost entirely inside frontier labs. The AI feature product manager builds a product or feature on top of an existing model, and needs domain expertise and user empathy far more than technical depth. Conflating the two is why the title has become so diluted.Using an LLM to think faster or write code faster does not make someone an AI product manager. It makes them a product manager using AI as part of their toolkit — the same as any other knowledge worker. The distinction matters because it clarifies what skills are actually being tested.Product principles have to come before evaluations, not after. Before Jonathan starts building an eval set for a new product, he first asks what the product is meant to feel like and what values it should encode. Google Search's response to sources disagreeing on a monument's construction date, or to large language models hallucinating at scale, came from principles about trustworthiness established before any metric was built.Trust in an AI feature is built through sourcing and interface design as much as through the model itself. Google Search's AI Overviews are constrained to draw only from ranked, trustworthy documents rather than the model's own memory, and users are given UX signals — citation placement, highlighting — that let them judge at a glance how much to verify.Synthetic users add genuine value in cold-start scenarios, privacy-sensitive research and automated regression testing. Where they fall short is diversity: AI-generated personas tend to be less varied and more extreme than real people, which is the central technical problem behind the Evens Foundation's digital citizenry project.There is no fixed answer to the right team size. Jonathan sees a gradient, from a senior developer working entirely alone, up to the Evens Foundation's single product manager with AI-assisted development skills, up to a fully staffed Google team — with the deciding factor being how unsolved the underlying problem is, not company size.As engineers absorb more evaluation work themselves through prompting and iteration, roles are blending. What still sits with product management is the judgement calls that follow from product principles — deciding, for example, which technical trade-offs actually matter to the use case, rather than which are easiest to measure.Features links- Evens Foundation — https://evensfoundation.eu- Makers Manifesto — https://makersmanifesto.org- Google DeepMind — https://deepmind.google- AutoGrid — smart grid AI company where Jonathan began applying machine learning to industry- Planet — satellite imagery company where Jonathan worked on automated road and building detectionWe're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Live from ONS 2026 in Stavanger, Jim sits down with Nicolay Ryste, VP of Product Management at Aize, the digital twin software company spun out of the Aker family. Nicolay traces Aize's path from an internal BP pilot to today's V2 platform running across Aker BP, ExxonMobil, and SBM Offshore, then previews V3: extending digital twins beyond topside to subsea and onshore assets, a rebuilt 3D and 2D engine built for tablets and lower-power devices, and a new MCP server that lets users bring their own AI agents into the twin. They also dig into how Aize is running a phased, side-by-side rollout so existing customers can adopt V3 without disrupting daily operations.

When AI can summarize meetings, draft updates, and even recommend priorities, what remains uniquely human about stakeholder alignment? In this episode, product coach and advisor Joy Adamson joins Matt and Moshe to explore why alignment is still one of the most essential, and most human, parts of a product manager's work.As part of our First Principles for PMs in the Age of AI series, Joy shares why stakeholder alignment is much more than keeping people informed or collecting feature requests. It is the ongoing work of connecting organizational needs with real customer and user needs, creating shared understanding of priorities, navigating competing incentives, and building enough trust to make difficult tradeoffs together.Join Matt and Moshe as they explore with Joy:What stakeholder alignment really means, and why true alignment reduces the need for constant stakeholder managementWhy the word “stakeholder” itself can create friction when people assume their request should automatically become a roadmap itemHow PMs can set healthy boundaries, say no with clarity, and prevent feature requests from becoming unchallenged commitmentsWhy emotional intelligence, negotiation, trust, and informal relationship-building remain critical product skillsThe difference between simply considering stakeholder input and actually aligning on priorities, tradeoffs, and resource allocationHow hidden motivations, incentives, politics, and organizational culture shape product decisions in ways AI cannot fully understandWhere AI can help: meeting summaries, follow-ups, tailored stakeholder communications, preparation, and negotiation supportWhy AI-generated recommendations still need human oversight, accountability, and judgment, especially when trust, risk, or ambiguity are involvedCould AI agents eventually become stakeholders themselves? What that might mean for product teams and decision-makingWhy junior PMs should deliberately build their human relationship and influence skills as AI takes on more operational tasksAnd much more!Want to connect with Joy?Website: https://www.digitaljoy.nl/LinkedIn: https://www.linkedin.com/in/adamsonj/You can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

This week, SAP's Atena Reyhani joins us to explore how agentic AI and SAP Business Network are enabling autonomous supply chains–from cross-enterprise orchestration to data-driven decisions. =====Atena Reyhani, Head of Product Management for SAP Business Network Core, joins us to unpack how agentic AI is transforming supply chains. We explore real-world use cases like order prioritization, the role of cross-enterprise data in AI decision-making, guardrails for responsible AI adoption, and why the future belongs to autonomous supply chains. ===== Guest 1: Atena Reyhani, Head of Product Management for SAP Business Network Core, SAPIn this role, Atena leads the AI strategy for the Network and helps to shape the path forward for our customers in becoming increasingly autonomous enterprises. The world's largest platform for business-to-business commerce and collaboration, SAP Business Network facilitates over $7.7 trillion in commerce through more than 910 million transactions annually, as of the 12 months ending Q2 2026. Prior to assuming the role in August 2025, Reyhani served as Chief Product Officer for ContractPodAi, spearheading the development of innovative enterprise and legal solutions – notably including Leah, a leading agentic AI solution for the enterprise legal sector. Renowned for her ability to turn bold ideas into transformative realities, Reyhani leads cross-functional teams to deliver exceptional AI solutions that redefine how enterprise work gets accomplished. Previously, Atena held leadership roles in product management, driving innovation in SaaS products for higher education at Pepperdine University and in the gaming and lottery industries at Diamond Game Enterprises.Host 1: Richard Howells⁠⁠⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.===== Show Links:SAP Business Network: https://www.sap.com/businessnetworkAtena Reyhani at SAP Connect in Las Vegas Session linkSupply Chain Management:  ⁠⁠⁠⁠⁠⁠⁠SAP Supply Chain Management⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠SAP Insights: Supply Chain⁠⁠⁠⁠⁠⁠⁠       Follow Us on Social Media : Atena Reyhani: LinkedInRichard Howells: ⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠SAP Digital Supply Chain: ⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠    Please give us a like, share, and subscribe to stay up-to-date on future episodes! 

Artificial intelligence is changing how product teams work, but not what good product management requires. As Arpan Podduturi explains, this remains true regardless of whether the AI integration involves physical products or software. Product teams in both environments face remarkably similar challenges: understanding user context, balancing trust and control, and delivering business value. Leveraging core product management principles remains critical to success. Arpan Podduturi is Vice President of Product at Samsara, where he leads strategy and development of the company's physical AI products. The Samsara product ecosystem is vast, serving back-office workers as well as truck drivers, machine operators, and front-line laborers. His perspective is practical: regardless of context, product teams still need to understand customers, identify valuable problems, prototype possible solutions, deliver measurable outcomes, and build trust. AI hasn't changed the job; it simply accelerates parts of the cycle. Here's what we learned: Product Discovery (Still) Starts With Customers For Samsara's product managers, discovery means going where users actually work – including factories, mines, airstrips, and oil refineries. Their approach to discovery in these physical environments is not much different from that in traditional software discovery; Arpan refers to it as forward-deployed engineering. “Our teams talk to users in their environment, and we work with them to figure out what problems they need help need solving,” he says. “It's not necessarily new; in fact, it's like the oldest song in the book.” Trust Remains Essential to the Product Experience When building tools that support physical safety, user trust is a core product requirement. The impact of AI integration is still a bit of an unknown – especially from the user perspective. Where personal safety is the desired outcome, Samsara product teams have to overcome a lot of suspicion about product performance. “We sell trust,” Arpan says, “and the product is about building trust and it’s about helping people get home safely at night.” But as AI models assume much of the responsibility for the build, it's imperative that humans are embedded throughout the product experience – from discovery through execution. AI Hasn't Changed the Job – Just the Set of Tools We Use To Do It AI can accelerate development, but product teams still have to demonstrate that new capabilities are worth adopting. Whether using AI or not, Arpan says “product managers still need to identify value, understand the market, prototype quickly, work with engineering, tell the right story, and share the ROI with the customer. This is still the job. AI just gives us a different set of tools.” [08:15] We sell trust. Our product is about building trust, and it’s about helping people get home safely at night.  [11:18] The software we build needs to conform to the business; the business should not conform to this software. [21:48] My highest level take on the way that product management has evolved over the last year and a half is that the PM job is the job.We still need to like identify value, understand the market, like prototype quickly, work with your engineering team, be able to tell the right story, share the ROI story of the customer. You still needed to do all those things. You just have a different set of tools. [28:33] The PM job hasn’t changed: building the right thing in the right way at the right time has always been the product person’s job. And it is now more than ever. [30:11] The product side and the craft side. So on the craft side, not a lot has changed. The tools are just better and everything is going faster. On the product side, there’s a lot that we are  discovering on the agentic front. And we are going to see these kind of massive digital transformations of physical operations companies. That’s changing rapidly as people are starting to understand that AI touching the physical world is actually the biggest opportunity before us. [31:47] The curious PM can use AI to take advantage of the “product pit stops.” Decisions come with so much context now, so if you’re a curious PM you can get the answer without having to wait for the data person to pull the thing or you don’t have the meeting with the customer to talk to them and get their feedback on a specific question. The post 196 / AI Changes the Tools, Not Product Management Fundamentals, with Arpan Podduturi appeared first on ITX Corp..

Someone just told you AI made roadmaps obsolete... and they're about to sell you the replacement!Product Manager Brian and Enterprise Business Agility Leader Om put "the Last Roadmap" pitch next to the Agile Manifesto and find a 25-year-old argument wearing new labels. In reality, the roadmap that 'died' is really a Gantt chart; the 'durable convictions' your company will be sold on will be the same tired top-down contracts you are handed today; and the AI-native team is the 2001 agile team word-for-word that gets pushed-and-then-pulled everytime the numbers or deadlines slip. Listen or watch as Brian and Om discuss and debate:Why the roadmap that 'died' was a Gantt chart wearing a product badgeThe 'funeral funnel' behind the pitch: declare it dead, sell the fixWhy 'durable convictions' set without customer collaboration are contracts with extra stepsThe AI-native team pitch (aka. the Agile Manifesto's empowered team from 2001)Why AI can't fix your empowerment problemsThis podcast is for product managers working at real companies, managers trapped in the messy middle, and anyone whose 'roadmap' has dates, swim lanes, and/or looking for a man in finance.#AgileManifesto #ProductRoadmap #AIAgile Manifesto (2001), Marty Cagan, Henry Gantt, Jira, W. Edwards Deming, L. David Marquet, Kumar Dattatreyan, Dario Amodei (Anthropic), Arguing Agile 249 (Disagree and Commit), Arguing Agile 113 (Outcome vs Output Roadmaps)LINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

In this episode of Cisco Champion Radio, we dive deep into the evolving landscape of Cisco's stack automation. As network complexity continues to grow, the need for simplified workflows and streamlined infrastructure management has never been more critical. We explore how Cisco is rising to the challenge by moving beyond individual component offerings to deliver complete, curated AI solutions. Key Highlights from This Episode: The Shift to Simplicity: Discover how Cisco's stack automation is transforming network management, enabling users to deploy curated solutions without the need for extensive coding expertise. Empowering Personas: Whether you are a "Consumer" looking to implement pre-built solutions or a "Producer" crafting custom blueprints, the platform provides a flexible, consistent framework for all your automation needs. The Power of Collaboration: Learn about the synergy between Cisco and Quality, and how this partnership enhances the stack automation ecosystem, making resource provisioning more efficient than ever. Openness and Extensibility: We discuss how Cisco's strategy has evolved to embrace openness, including seamless integration with third-party products to expand functionality. A New Approach to AI: Get a preview of the upcoming October release, which shifts the focus toward providing complete AI solutions, simplifying the purchasing experience for our customers. Governance and Workflows: We break down the technical distinction between stateless workflows and stateful management, providing insights on how to maintain effective infrastructure governance. Join our panel of experts as we discuss how these advancements are not just changing the way we scale networks today, but how they are paving the way for the future of AI-driven Learn more about Stack Automation: https://www.cisco.com/site/us/en/solutions/data-center/stack-automation-quali/index.html Create an account and start a trial: https://stackautomation.cisco.com/login Cisco guests Carlos Campos Torres, Senior Director, Product Management. – AI, Compute, and Ecosystem Software, Cisco Pablo Urcid, Product Manager – AI, Compute, & Ecosystem Automation, Cisco David Ben Shabat, VP R&D, Quali Cisco Champion hosts Liam Keegan, VP of Technology, RX3 Communications, Inc. Dan Wade, Practice Lead, Network & Infrastructure Automation Marco Krauss, IT Senior Consultant Network Automation, Computacenter Moderator Danielle Carter, Cisco, CCR Program/ Customer Voices

Isobel Handler, Senior Director of Product Management at Kontakt.io addresses critical inefficiencies in clinical exam room utilization.  By capturing data about staff-patient interactions and facility usage, the Kontakt.io platform incorporates real-time location data to optimize patient access, reduce waiting time, and improve staff safety. The solution helps healthcare organizations understand their current capacity utilization before investing in new facilities and supports integration of digital tools to include virtual visits and asychronous communication to better serve patients and reduce clinician burnout.    Isobel explains, "Kontakt.io uses real-time location data, often referred to as RTLS, to improve hospital and clinic operations. We'll talk a lot today about our ambulatory use cases focused on patient access. Still, we also have solutions for inpatient flow, length-of-stay reduction, staff safety, asset tracking, and much more that I won't list. But I think what's really exciting about Kontakt.io is you'll see a lot of RTLS companies in the industry who have built solutions that provide really beautiful blue dots on a map. But at Kontakt.io, we see the opportunity to leverage RTLS beyond that blue dot to produce a set of data that's really well trained and tuned to feed AI models. RTLS data is plentiful, it's machine-generated, it's unbiased, and it doesn't require a human being with a clinical license to enter it. So it produces a lot of really useful information for AI models that are solving some of the top health system problems out there today."   "I think it's an environment that's really ripe for some more data to help us understand what's going on in the exam room, as you pointed out. But a shocking statistic is that most exam rooms are only utilized 33% of the time during standard business hours, which feels incredibly low the first time you hear it. But if you think through the logic of how we get to that utilization rate, the performance actually seems quite intuitive." #KontaktIO #IntelligentOrchestration #HospitalOperations #HealthcareAI #PatientAccess #DigitalHealth #HealthcareInnovation #RTLS #AmbulatoryCare #HealthIT #ClinicianBurnout #HealthcareLeadership  Kontakt.io Download the transcript here  

Isobel Handler, Senior Director of Product Management at Kontakt.io addresses critical inefficiencies in clinical exam room utilization.  By capturing data about staff-patient interactions and facility usage, the Kontakt.io platform incorporates real-time location data to optimize patient access, reduce waiting time, and improve staff safety. The solution helps healthcare organizations understand their current capacity utilization before investing in new facilities and supports integration of digital tools to include virtual visits and asychronous communication to better serve patients and reduce clinician burnout.    Isobel explains, "Kontakt.io uses real-time location data, often referred to as RTLS, to improve hospital and clinic operations. We'll talk a lot today about our ambulatory use cases focused on patient access. Still, we also have solutions for inpatient flow, length-of-stay reduction, staff safety, asset tracking, and much more that I won't list. But I think what's really exciting about Kontakt.io is you'll see a lot of RTLS companies in the industry who have built solutions that provide really beautiful blue dots on a map. But at Kontakt.io, we see the opportunity to leverage RTLS beyond that blue dot to produce a set of data that's really well trained and tuned to feed AI models. RTLS data is plentiful, it's machine-generated, it's unbiased, and it doesn't require a human being with a clinical license to enter it. So it produces a lot of really useful information for AI models that are solving some of the top health system problems out there today."   "I think it's an environment that's really ripe for some more data to help us understand what's going on in the exam room, as you pointed out. But a shocking statistic is that most exam rooms are only utilized 33% of the time during standard business hours, which feels incredibly low the first time you hear it. But if you think through the logic of how we get to that utilization rate, the performance actually seems quite intuitive." #KontaktIO #IntelligentOrchestration #HospitalOperations #HealthcareAI #PatientAccess #DigitalHealth #HealthcareInnovation #RTLS #AmbulatoryCare #HealthIT #ClinicianBurnout #HealthcareLeadership  Kontakt.io Listen to the podcast here

Every day, more of your traffic isn't human. AI agents show up, grab what they need, and leave — and YOU pay to serve every visitor, whether they see your brand or not. Today's guest has seen this from both sides. Brandon Harris is VP of Product at Pantheon, the WebOps platform behind a huge slice of the WordPress and Drupal world. But before that, he was sending the bots — at Wiser, where his team scraped millions of pages a day to power retail pricing intelligence. His take: agents aren't killing the web. They're just the next way information travels — and the companies that adapt are about to pull way ahead of the ones that don't. In this episode, Brandon shares: How AI is turning the whole web into one giant CMS — and quietly rewriting the job of anyone who publishes content Plus, why visits are the new impressions, why trying to keep agents out is the modern-day version of blocking Google from indexing your site — and what to do instead Links LinkedIn: https://www.linkedin.com/in/cxoplus/ Pantheon: https://pantheon.io/ Chapters 00:00 Introduction 01:02 Brandon's product journey: From film school to Wiser and Pantheon 02:24 The dead web debate: Agents are just the next mode of transport 05:51 Why traffic-based pricing can break when the majority of your visitors are agents 08:15 Site visits are becoming the new impressions 10:46 Grading traffic and the identity arms race it will start 14:11 When the whole web becomes your CMS 16:40 Putting your brand's fingerprints in content that AI will pull without accessing your site 21:23 How people decide to trust a site 26:17 Watching sessions to catch user drift before customers quietly leave 31:09 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Brandon Harris.

AI Wrote the PRD. You're Still Accountable for It. Seven pages out of the model in minutes. Formatted, confident, and not necessarily right. Advaita Nigudkar has spent eight years at BILL and helped launch the company's first agentic AI platform, in a category where a wrong number means somebody's money moved. She sits down with Rina Alexin to talk about the context work that makes AI output usable, using an LLM as a judge to hold a quality bar across growing teams, and the one PM quality she thinks AI can quietly take from you. Key Topics Discussed in This Episode Garbage in, garbage out is now a job description One-line prompts produce PRDs that solve the admin use case and ignore everyone else. Advaita's team built templates and skills that interrogate the PM first: which customer, which SKU, which partner type. An LLM as your PRD judge Feed the model your best past PRDs as the gold standard and let it flag what's missing. The PM supplies the substance. The model finesses it. Consistency survives headcount growth and churn. The trust feature you hope nobody opens BILL's agents move money, so the team shipped an activity log and on/off controls. Engagement dropped once customers trusted it. That was the point. Why Listen to This Episode? In this episode, you'll get: A repeatable way to raise the bar on AI-written PRDs, using past work as the standard instead of vibes The context you have to supply before AI is useful: personas, permissions, business types, historical decisions A trust playbook for AI features in regulated environments, from encrypted data handling to auditability and kill switches A framework for what stays human: negotiation, cross-functional trade-offs, and the judgment behind which problems deserve solving Plus the warning Advaita gives every PM who gets comfortable with easy answers. Related Resources Check out these additional tools and resources to add to your PM belt: Productside Resource Library More Productside Stories Podcast Episodes Explore Productside Courses 

Episode web page: https://bit.ly/4ywaHhh Episode summary In this episode of Insights Unlocked, Mike Mace, Director of Solution Marketing at UserTesting, talks with veteran product leader, author, and executive coach Rich Mironov about what AI-powered development really means for product teams—and why dramatically faster coding doesn't automatically translate into better products, happier customers, or more revenue. Rich argues that as AI removes engineering constraints, the bigger challenge becomes deciding what is actually worth building. He explores the risks of “cognitive surrender,” where teams equate faster output with better outcomes, and explains why product management, UX research, customer discovery, business judgment, and taste become more important—not less—as organizations gain the ability to build at unprecedented speed. He also challenges the growing enthusiasm for synthetic users, warning that plausible AI-generated feedback can reinforce what teams already believe rather than uncover the unexpected insights that emerge from conversations with real customers. The conversation also examines why “10x coding speed” won't produce 10x revenue, how AI shifts bottlenecks from engineering toward customer adoption and go-to-market execution, and why product managers may need to become more “barbell shaped”—spending more time understanding meaningful customer problems at the front end and turning products into business results at the other. Rich also shares ideas from his book Money Stories, including why product teams need to communicate the financial value of their work in language executives understand. You'll learn Why faster AI-assisted coding doesn't necessarily create better products or more revenue How “cognitive surrender” can cause teams to prioritize output over customer and business outcomes Why human discovery, judgment, empathy, and taste become more valuable as building gets easier The risks of replacing conversations with real customers with synthetic users Why product waste is fundamentally different from engineering waste How AI shifts product bottlenecks toward discovery, customer adoption, sales, and go-to-market execution Why product managers may need to become more “barbell shaped” in an AI-driven environment How “money stories” can help product teams connect their work to revenue and business impact Resources and links Rich on LinkedIn (https://www.linkedin.com/in/richmironov/) Rich's Product Bytes (https://www.mironov.com/) and Substack (https://richmironov.substack.com/)  The Art of Product Management (https://www.amazon.com/Art-Product-Management-Lessons-Innovator/dp/1439216061) Money Stories: Communicating the Value of Product Work (https://www.amazon.com/Money-Stories-Communicating-Value-Product-ebook/dp/B0GJTS2CW6) Our past interview with Rich on product waste and how to prevent it (https://www.usertesting.com/blog/how-prevent-product-waste) Mike Mace on LinkedIn (https://www.linkedin.com/in/mikemace/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked (https://www.usertesting.com/resources/podcast)

“AI is no longer just a chatbot. It is becoming a proactive coworker.” In this Technology Reseller News podcast, Hardik Ajmera, Vice President of Product Management at Extreme Networks, discusses the general availability of Extreme Agent One Coworker, a context-aware AI agent designed specifically for enterprise networking. Extreme Networks provides secure connectivity across enterprise environments, including wired and wireless networking, SD-WAN, campus, branch and data center infrastructure. Ajmera says the key limitation of generic AI in networking is context. A general AI model can explain networking concepts or suggest troubleshooting steps, but it does not automatically understand a customer's topology, historical trends, policies, operating procedures or live network conditions. Extreme Agent One is designed to add that context. Built into Extreme Platform One, the AI continuously monitors the network, analyzes current and historical data and can proactively surface important issues before an engineer asks for help. “If it sees a severity-one issue, it does the legwork first,” Ajmera says. “It gathers the data, understands the impact and comes back with recommended steps.” The system can also help with troubleshooting, configuration guidance, knowledge-base access, evidence collection and support-case creation. For network teams, that means moving from reactive management toward a more proactive operating model. Ajmera says governance remains central to that transition. Extreme Agent One provides visibility into what data it used, how it reasoned through a problem and why it is recommending a particular action. Customers can also determine how much authority they want to give the AI. “Humans are still in control,” Ajmera says. “The key is that customers need a choice.” Extreme sees repetitive, time-consuming tasks such as issue analysis, troubleshooting and case creation as natural early candidates for AI automation, with more autonomous network operations developing over time as customers build trust. Extreme Agent One Coworker became generally available worldwide on August 31 and is available through Extreme Platform One. Visit ExtremeNetworks.com to learn more  

Scott Spencer is the CEO and Co-founder of Rewarded Interest, a privacy-first identity platform and browser extension designed to eliminate cookie consent fatigue and restore consumer control over digital tracking.Before founding Rewarded Interest, Spencer spent over 25 years shaping the programmatic advertising landscape. At DoubleClick, LLC, he developed the industry's initial ad exchange before its acquisition by Google. During his 15-year tenure at Google, he served as Vice President of Product Management for Ads Privacy & Safety, where he built global anti-fraud systems, created the Coalition for Better Ads, and spearheaded initiatives to align ad targeting with user trust.

We're back with a special host-only episode! Vidya Dinamani and Heather Samarin step out of the guest chair to unpack what they're hearing across nearly twenty AI roundtables they ran with CPOs, VPs, and product leaders across the industry.Why the PRD is giving way to context, how the PM-to-engineer ratio is flipping, the pull toward a shared context layer, and why there's still no one-size-fits-all for AI.If you're a product leader silently wondering whether you're behind on all this, the genuine answer from the room is that everyone is, and that should bring you some comfort.Plus the persona skill one leader built to stop the slop.

Un bon PMM ne se contente plus d'appliquer des frameworks.Pour Capucine Roche, CEO de Letsignit, il doit comprendre le produit de bout en bout, rester proche des deals et être obsédé par le business.Dans cet extrait, elle partage les qualités qu'elle recherche aujourd'hui chez un PMM et pourquoi il faut recruter dès la phase d'early stage.Dans l'épisode complet, nous prenons du recul sur le Product Marketing à travers le regard d'une CEO, ancienne responsable marketing et impliquée depuis des années dans les sujets produit.Au programme :

Your company cut the Scrum Master role and called it self-organization. Fantastic... Where did the coordination work go? We'll bet it landed on someone who doesn't get paid to do it.Product Manager Brian Orlando and Enterprise Business Agility Leader Om Patel argue through what happens when organizations defund facilitation roles but keep the complex frameworks. Stick with as Brian tries to discern if "let the team figure it out" is really just financial code for a transfer of labor from funded to unfunded and stay till the end to understand how to spot the competence tax on your own team.Listen or watch as we discuss:• Why "automate it with AI" and "let the team self-organize" are the same failure• What is the competence tax: being good at unfunded coordination work• Who non-promotable tasks disproportionately land on and why• How NOBODY EVER accidentally rolled out SAFe and "forgot" to fund rolesIf you're an Engineer, Product Person, Scrum Master, or Agile Coach watching coordination work get quietly dumped onto the wrong people, this episode is for you! I fight for the users!#ScrumMaster #Agile #CompetenceTaxTanya Reilly (Being Glue, Lead Dev talk), Winning with People by John Maxwell, Babcock and Vesterland (gender differences in non-promotable tasks), SAFe, SquarespaceLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

“Product sense” is the thing that nobody can define, but everyone says will keep product managers employed in the age of AI. Our guest today has a definition, with receipts. But he takes it somewhere that's less comfortable than you might assume: if all you do is move numbers, you're obsoleting yourself because AI will eventually do it better than you can. Kevin Sung is VP of Product at Life360, the family safety app with roughly 100 million monthly active users. Before that, he spent years at Dropbox, Smule, Zynga, and Google, a path that took him from a growth-hacking playbook to a very different conclusion about where product management's value actually lives. In this episode, Kevin discusses: What the PM job becomes once engineering isn't a bottleneck, and why the line between lazy product management and good product craft is the strength of your opinion How he got Dropbox leadership to fund a year of unglamorous quality work that no financial model could justify, and how it produced $24 million in ARR from churn reduction And what this all means now at Life360 — where the question in a product review isn't “how's this move the number”, it's “does this create a moment of worry for a parent?” Links LinkedIn: https://www.linkedin.com/in/kevinsung/ Life360: https://www.life360.com/ Chapters 00:00 Introduction 04:06 Defining "product sense" 07:45 How the PM role is evolving 10:20 Kevin's growth lessons from Smule 14:22 The $24M retention bet 18:29 Resentment builds until users snap 22:38 Selling a bet you can't model 24:36 Enshittification and paper cuts 27:51 Life360, where trust is the product 32:23 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Kevin Sung.

In this episode Robin Olds interviews Tom Footitt, Cisco Senior Director of Product Management for Crosswork Assurance, about how AI — especially agentic systems and faster LLMs — is changing network traffic patterns and why observability and assurance are critical. They discuss new KPIs like time-to-first-token and inter-token latency, passive behavioral analysis of encrypted traffic, predictive AIOps, and the importance of security, sovereignty, and performance for service providers offering AI services.

While AI has transcended the hype stage in financial services, the truth remains that some banks have more of a grip on how to use it than others. Whether in the back office or on the front lines, AI remains a mystery to some, a must-have for others and an ROI crap shoot for many. Join us as five fintech experts weigh in on the state of AI in Fall 2026, each assigning grades to the industry you won't want to miss. Our Guests: Matt Creatore, Chief Customer & Growth Officer, Titan Danial Jameel, Founder/CEO Saris AI Mitch Rutledge, Co-founder/CEO, Vertice AI Mac Thompson, Founder and CEO, White Clay Matthew Wood, Sr. Director of Product Management, Banking and Financial Services, Tavant

Cole Conrad, Senior Vice President of Product Management at Milwaukee Tool, breaks down how Milwaukee Tool's growth into transportation trades wasn't a step-by-step plan—it was patience and instinct, one tool at a time. In this episode, recorded live at Milwaukee Pipeline 2026, Cole covers the shift into automotive as an all-in focus, how the team decides what's worth building next, and why trusting instinct over data was key to bets like the Hammer Chisel tool.Watch the video recording on YouTubeAbout the EpisodeHost: Jay Goninen, ASE, jgoninen@ase.comGuest: Cole Conrad, Milwaukee Tool, Cole.Conrad@milwaukeetool.comLinks & ResourcesGet notified of new episodes --> Join our email listMilwaukee PipelineJoin the ASE Connects CommunityASE Connects brings shops, dealerships, and schools together in one structured network to strengthen the technician pipeline. By making it easier to connect, collaborate, and support students through job shadows, internships, and classroom engagement, ASE Connects helps schools build stronger programs and helps shops develop a more consistent, local source of future technicians. Learn more:ASE Connects Memberships for Shops & Dealers (Free trial for shops until 12/31/26)ASE Connects Memberships for Schools (Free for schools)Connect with us on social:FacebookInstagramXLinkedInYouTubeTikTok

We're back with a familiar voice on the podcast: John Fontenot returns to continue our *First Principles for PMs in the Age of AI* series, this time focused on Vision and Strategy. In a conversation that blends product thinking, personal reflection, and a healthy dose of AI skepticism, John helps us explore what it really means for PMs to define where they're going, and how to get there.John defines vision as the desired end state or future outcome, and strategy as the path, phases, and tradeoffs required to reach it. With examples ranging from long-term business goals to the realities of regulated industries, he explains why product managers still need to lead on vision and strategy even when leadership doesn't provide a clear direction, and why AI can support this work, but not fully own it.Join Matt and Moshe as they explore with John:What vision and strategy actually mean in product management, and why they're easy to confuse  - How to connect a big vision to practical milestones, obstacles, and key results  Why AI can help with analysis and brainstorming, but can't replace human creativity, empathy, or rule-breaking thinking  - The limits of AI in regulated spaces like finance and healthcare, where context and compliance matter  Why PMs often have to create strategy when the organization doesn't hand it to them  - How to use AI as a thinking partner without outsourcing your strategic judgment  The balance between staying anchored in the long-term vision and adjusting to the realities of the market, user needs, and constraints  John's perspective on where product teams should be careful not to let AI flatten nuance or over-automate strategy  And much moreWant to connect with John?LinkedIn: https://www.linkedin.com/in/johnrfontenot/  You can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/  Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

What does the future of financial advice look like when AI, automation, and modern technology reshape how people manage their money? In this episode of Boldin Your Money, Steve Chen sits down with Subbiah Subramanian, VP of Product Management at Clearwater Analytics, to explore how technology is transforming wealth management, financial planning, and the advisor-client relationship. They discuss the recent Vanguard–Altruist acquisition, the growing advice gap, how AI can empower both advisors and consumers, and why the future of financial planning will likely combine intelligent technology with human expertise. Whether you're interested in retirement planning, fintech, or the future of AI in personal finance, this conversation offers valuable insights into where the industry is headed.Connect with Boldin:Build your retirement plan at Boldin.comWatch more retirement education and expert conversations on the Boldin YouTube channelFollow Boldin Your Money for weekly conversations on retirement, investing, financial planning, and the future of personal finance.

At dating app The League, Kunal Thadani's team found that when daily matches took longer to load, people stopped skimming profiles and started actually reading. The wait wasn't a cost — it was the product saying these matches are worth your time. Now he's Senior Director of Product at Houzz, a platform for homeowners to plan renovations, and his take is that the goal was never no friction. It's to create the right kind of friction. In this episode, Kunal drops his: His three-part framework for productive friction, and The two ways friction turns toxic, including the one that punishes your most loyal customers Plus, everyone is building AI workflows for themselves right now. Almost nobody is building them for a team, and Kunal has the clearest answer I've heard in a long time for what that looks like. Links LinkedIn: https://www.linkedin.com/in/kunal-thadani-72a13722/ Insider Growth Group: https://www.insidergrowthhq.com/ Houzz: https://www.houzz.com/ 0:00 Introduction 0:28 From The League to Houzz: Finding the DIY Decorating Gap 2:03 Not All Friction Is Bad 4:39 Trust Signals, Progress Arcs, Commitment Moments 6:41 The Signup Test That 10X'd Email Conversions 10:25 Two Ways Friction Fails: Fake Protection and Endless Re-Verification 15:16 Why Personal AI Setups Break Down at Team Scale 16:47 Inside the Team OS: One Repo and a Shared Product Context Layer 20:38 Claude Code in the Terminal: Mining Gong Calls and Killing Handoff Tax 24:41 Feedback Loops: Why a Team OS Improves Faster Than a Personal One 25:58 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Kunal Thadani.

What happens when coding stops being a constraint for product teams, and what should product leaders do right now to get ready? In this episode of the CPO Rising series hosted by Products That Count Resident CPO Renee Niemi, Tubi Chief Product and Technology Officer Mike Bidgoli will be speaking on why this is a golden era for product management and how the entertainment industry is about to go through its biggest disruption yet. Mike shares how Tubi runs "Tinker Days" to keep the whole org experimenting, why he's investing countercyclically in junior talent, and what it means to build for model capabilities that don't exist yet.

Nick Brown and Smith Freeman of SVS Sound On today's show we have an interview with Nick Brown (VP of Marketing) and Smith Freeman (Senior Director of Product Management) at SVS Sound.  We discuss their new Auto EQ, the upcoming Soundbar and general audio chat. SVS Launches First-Ever Soundbar and New 3000 R|Evolution Series Subwoofers at CES 2026 SVS AUTO EQ Frequently Asked Questions

In this episode, Madelyn O'Farrell interviews Eesha Pathak, Senior Director of Product Management at Crusoe, about her unconventional career from software engineering and branding to leading enterprise AI at Google and now building AI-first infrastructure. They discuss Crusoe's vertically integrated, energy-first approach to cloud and data centers, capturing abundant energy (like stranded gas and renewables) and turning “electrons into intelligence” via GPUs and a custom cloud stack. Eesha explains how Crusoe's gigawatt-scale campuses and modular Spark edge deployments complement each other to deliver both scale and low-latency inference, dives into the Managed AI platform with its model marketplace, self-serve and tailored deployments, fine-tuning, and upcoming reinforcement learning, and highlights why judgment and holistic thinking are critical in product and engineering. She also unpacks Crusoe's close partnership with NVIDIA, its role in enabling physical AI and robotics with low-latency compute, a flexible data and partnership strategy, and her long-term vision of Crusoe becoming the most helpful company for people building AI, so customers can focus on their products instead of wrestling with infrastructure. Highlights from their conversation include: Eesha's Unconventional Career Path from Bosch to Google to Crusoe (0:29) Why Energy Is the Real Bottleneck for AI Infrastructure (3:30) What Energy-First Means and Bringing Compute to Abundant Power (5:04) Spark Modular Data Centers and Edge Zones Strategy (6:22) How Gigawatt Campuses and Edge Zones Work Together (8:26) Importance of Judgment in Product and Engineering Decisions (12:49) NVIDIA Partnership and Day Zero Nemotron Model Launches (15:25) Physical AI, Robotics, and Low-Latency Edge Inference (17:21) Crusoe's Data Strategy and Partnership Approach (21:52) Vision for Crusoe as Most Helpful Company for AI Builders (22:47) Closing Thoughts and Episode Wrap-Up (24:09) Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at www.dynamo.vc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Some of the best manufacturing advice comes down to one simple idea - buy back your weekends.In this bonus episode of Manufacturing Happy Hour, Chris is joined by Patrick Carmitchel, VP of Product Management and Dustin Moore, Condition Monitoring Engineer Manager at AssetWatch, to break down what condition monitoring and predictive maintenance looks like in practice, no jargon required.They get into the differences between preventative and predictive maintenance, why the biggest ROI often comes from just doing one thing well, and how AssetWatch's own founding story led them from wireless power research into one of the fastest-growing names in industrial monitoring. Along the way, Dustin shares a personal philosophy that gives this episode its name.In this episode, find out:Why doing one thing well with your most critical assets can catch the majority of issues before you need to expand into other monitoring methodsHow a maintenance technician went from feeling threatened by AssetWatch to calling it the best thing that ever happened to his jobWhat criteria actually determine whether an asset is "critical" - and why size has nothing to do with itHow AssetWatch went from researching wireless phone charging to building one of the fastest-growing platforms in industrial monitoringEnjoying the show? Please leave us a review here. Even one sentence helps. It's feedback from Manufacturing All-Stars like you that keeps us going!Tweetable Quotes:"The proof is in the pudding... once they start seeing the results on their end - that bearing running a lot cooler, a lot smoother - that's when they come around." - Dustin Moore, Condition Monitoring Engineer Manager at AssetWatch"You've got to start somewhere. Meet maintenance teams where they're at - they're all in different parts of their journey." - Dustin Moore, Condition Monitoring Engineer Manager at AssetWatch"Predictive maintenance means you're looking at early warning signs... it's about your ability to be able to plan so that you do not have unplanned downtime." - Patrick Carmitchel, VP of Product Management at AssetWatchLinks & mentions:AssetWatch combines vibration, temperature, and oil analysis in one platform for a holistic asset health view, detecting issues that a single technique might miss.Make sure to visit https://manufacturinghappyhour.com for detailed show notes and a full list of resources mentioned in this episode. Stay Innovative, Stay Thirsty.